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Forecast Combinations

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Author Info
Timmermann, Allan G
Abstract

Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the degree of correlation between forecast errors and the relative size of the individual models’ forecast error variances). Although the reasons for the success of simple combination schemes are poorly understood, we discuss several possibilities related to model misspecification, instability (non-stationarities) and estimation error in situations where the numbers of models is large relative to the available sample size. We discuss the role of combinations under asymmetric loss and consider combinations of point, interval and probability forecasts.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 5361.

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Date of creation: Nov 2005
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Handle: RePEc:cpr:ceprdp:5361

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Keywords: diversification gains forecast combinations model misspecification pooling and trimming shrinkage methods

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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  6. Patton, Andrew J & Timmermann, Allan G, 2003. "Properties of Optimal Forecasts," CEPR Discussion Papers 4037, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  7. Bunn, Derek W., 1985. "Statistical efficiency in the linear combination of forecasts," International Journal of Forecasting, Elsevier, vol. 1(2), pages 151-163. [Downloadable!] (restricted)
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  11. Raffaella Giacomini & Ivana Komunjer, 2002. "Evaluation and Combination of Conditional Quantile Forecasts," University of California at San Diego, Economics Working Paper Series 2002-11, Department of Economics, UC San Diego. [Downloadable!]
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  12. repec:cup:etheor:v:13:y:1997:i:6:p:808-17 is not listed on IDEAS
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  21. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1684, 08. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Barbara Rossi, 2005. "Are Exchange Rates Really Random Walks? Some Evidence Robust to Parameter Instability," International Finance 0503006, EconWPA. [Downloadable!]
    Other versions:
  2. Zhuo Chen & Yuhong Yang, 2007. "Time Series Models for Forecasting: Testing or Combining?," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 11(1), pages 1385-1385. [Downloadable!] (restricted)
  3. Massimo Guidolin & Carrie Fangzhou Na, 2007. "The economic and statistical value of forecast combinations under regime switching: an application to predictable U.S. returns," Working Papers 2006-059, Federal Reserve Bank of St. Louis. [Downloadable!]
  4. Marcellino, Massimiliano, 2005. "Pooling-based data interpolation and backdating," CEPR Discussion Papers 5295, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  5. Richard A. Ashley. & Randall J. Verbrugge., 2006. "Mis-Specification and Frequency Dependence in a New Keynesian Phillips Curve," Working Papers e06-12, Virginia Polytechnic Institute and State University, Department of Economics. [Downloadable!]
  6. Serena Ng & Matt Shum, 2007. "Detecting Information Pooling: Evidence from Earnings Forecasts after Brokerage Mergers," Advances in Economic Analysis & Policy, Berkeley Electronic Press, vol. 7(1), pages 1801-1801. [Downloadable!] (restricted)
  7. Massimo Guidolin & Allan Timmerman, 2007. "Forecasts of U.S. short-term interest rates: a flexible forecast combination approach," Working Papers 2005-059, Federal Reserve Bank of St. Louis. [Downloadable!]
    Other versions:
  8. Joshua Gallin & Randal Verbrugge, 2007. "Improving the CPI’s Age-Bias Adjustment: Leverage, Disaggregation and Model Averaging," Working Papers 411, U.S. Bureau of Labor Statistics. [Downloadable!]
  9. Emil Stavrev, 2006. "Measures of Underlying Inflation in the Euro Area: Assessment and Role for Informing Monetary Policy," IMF Working Papers 06/197, International Monetary Fund. [Downloadable!]
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